Image saliency detection method based on multifeature maps fusion
In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics."
Authors
- Xiaoli Li (ORCID: https://orcid.org/0000-0001-9113-7130)
- Yunpeng Liu (ORCID: https://orcid.org/0000-0002-0959-0209)
- huaici zhao
Institutions
- Shenyang Institute of Automation (CN)
- Guangdong Institute of Intelligent Manufacturing (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1117/12.3045835
- Primary Topic
- Visual Attention and Saliency Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00